Applied AI/ML - Executive Director
LONDON, United Kingdom
JPMorgan Chase & Co.
We are thrilled to introduce you to our team at the Chief Data and Analytics Office (CDAO) organization. As the driving force behind the firmwide adoption of artificial intelligence (AI) across our company, our dedicated team is responsible for overseeing data use, governance, and controls around the build, adoption and maintenance of cloud infrastructure, data and AI/ML products. With a focus on both effectiveness and responsibility, we strive to push the boundaries of innovation while ensuring ethical and sustainable practices. Join us on this exciting journey as we revolutionize the way we leverage data and analytics to shape the future of our organization.
As a LLM (Large Language Models) ML Ops Engineering Executive Director within our CDAO organization, you will play a crucial role in ensuring the smooth operation and optimization of our LLM aided AI products. Our firm-wide team focuses on developing scalable LLM-based products and reusable back-end APIs. You will engage in close collaboration with cross-functional teams, including the ML Centre of Excellence, AI Research, Cloud Engineering, and others, to foster innovation and deliver solutions that yield a high Return-on-Investment (RoI). You will ensure that our APIs are built with scalability in mind, allowing them to efficiently handle a large number of requests without compromising performance. By designing APIs with a clear separation of concerns and well-defined interfaces, we enable other teams and developers to leverage our APIs to build their own ML products and solutions, fostering a culture of collaboration and efficiency.
Required qualifications, capabilities, and skills
- PhD in a quantitative discipline, e.g. Computer Science, Mathematics, Statistics.
- 10+ years of experience in an individual contributor role in ML engineering.
- Proven track record in building and leading teams of experienced ML engineers/scientists.
- Solid understanding of the fundamentals of statistics, optimization, and ML theory, focusing on NLP and/or Computer Vision algorithms.
- Hands-on experience in implementing distributed/multi-threaded/scalable applications (incl. frameworks such as Ray, Horovod, DeepSpeed, etc.).
- Ability to understand and align with business expectations, and write clear and concise OKRs (Objectives and Key Results).
- Experience as a "Responsible Owner" for ML services in enterprise environments.
- Excellent grasp of computer science fundamentals and SDLC best practices.
- Ability to understand business objectives and align ML problem definition.
- Strong communication skills to effectively convey technical information and ideas at all levels, building trust with stakeholders.
Preferred qualifications, capabilities, and skills
- Experience in designing and implementing pipelines using DAGs (e.g., Kubeflow, DVC, Ray).
- Ability to construct batch and streaming microservices exposed as gRPC and/or GraphQL endpoints.
- Demonstrable experience in parameter-efficient fine-tuning, model quantization, and quantization-aware fine-tuning of LLM models.
- Hands-on knowledge of Chain-of-Thoughts, Tree-of-Thoughts, Graph-of-Thoughts prompting strategies.
We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants’ and employees’ religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.
* Salary range is an estimate based on our AI, ML, Data Science Salary Index 💰
Tags: APIs Computer Science Computer Vision Engineering GraphQL Horovod Kubeflow LLMs Machine Learning Mathematics Microservices NLP OKR PhD Pipelines Prompt engineering Research SDLC Statistics Streaming
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